Digital image understanding includes several levels, such as image segmentation, edge detection, and image target detection and recognition. Image target detection and recognition is used to recognize people or an object in an image, and perform classification tagging for the image (for example, areas in the image are separately tagged as sky, beach, sun, and the like). A most typical type of problem is recognizing a type of object in an image, for example, a Caltech101 dataset is a similar problem. Image target detection and recognition is one of main issues in the field of computer vision, and also an important breakthrough point in the field of artificial intelligence.
Currently, a target detection method is usually implemented by fixing a type of object, modeling for a shape or an edge (even a bounding box) of the object, scanning a position of the object in an image, and performing fitting. Edge detection may be implemented using a method such as the Canny operator. Shape or edge modeling and tracing may be implemented using a method such as condensation, Kalman filter, or mean shift.
Because target detection is usually used to determine a known type of object (such as a human face, a human body, and a particular type of object), understanding of an object of an unknown class is not involved. If a new target is not included in objects that need to be traced, it is difficult to determine the target.